A divide and conquer strategy for scaling weather simulations with multiple regions of interest.

Abstract

Accurate and timely prediction of weather phenomena, such as hurricanes and flash floods, require high-fidelity compute intensive simulations of multiple finer regions
of interest within a coarse simulation domain. Current weather
applications execute these nested simulations sequentially using
all the available processors, which is sub-optimal due to their sublinear scalability. In this work, we present a strategy for parallel
execution of multiple nested domain simulations based on partitioning the 2-D processor grid into disjoint rectangular regions
associated with each domain. We propose a novel combination
of performance prediction, processor allocation methods and
topology-aware mapping of the regions on torus interconnects.
Experiments on IBM Blue Gene systems using WRF show that
the proposed strategies result in performance improvement of up
to 33% with topology-oblivious mapping and up to additional
7% with topology-aware mapping over the default sequential
strategy